Geant4 Cross Reference

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Geant4/examples/extended/parameterisations/Par04/training/tune_model.py

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  1 from argparse import ArgumentParser
  2 
  3 from core.constants import MAX_GPU_MEMORY_ALLOCATION, GPU_IDS
  4 from utils.gpu_limiter import GPULimiter
  5 from utils.optimizer import OptimizerType
  6 
  7 # Hyperparemeters to be optimized.
  8 discrete_parameters = {"nb_hidden_layers": (1, 6), "latent_dim": (15, 100)}
  9 continuous_parameters = {"learning_rate": (0.0001, 0.005)}
 10 categorical_parameters = {"optimizer_type": [OptimizerType.ADAM, OptimizerType.RMSPROP]}
 11 
 12 
 13 def parse_args():
 14     argument_parser = ArgumentParser()
 15     argument_parser.add_argument("--study-name", type=str, default="default_study_name")
 16     argument_parser.add_argument("--storage", type=str)
 17     argument_parser.add_argument("--max-gpu-memory-allocation", type=int, default=MAX_GPU_MEMORY_ALLOCATION)
 18     argument_parser.add_argument("--gpu-ids", type=str, default=GPU_IDS)
 19     args = argument_parser.parse_args()
 20     return args
 21 
 22 
 23 def main():
 24     # 0. Parse arguments.
 25     args = parse_args()
 26     study_name = args.study_name
 27     storage = args.storage
 28     max_gpu_memory_allocation = args.max_gpu_memory_allocation
 29     gpu_ids = args.gpu_ids
 30 
 31     # 1. Set GPU memory limits.
 32     GPULimiter(_gpu_ids=gpu_ids, _max_gpu_memory_allocation=max_gpu_memory_allocation)()
 33 
 34     # 2. Manufacture hyperparameter tuner.
 35 
 36     # This import must be local because otherwise it is impossible to call GPULimiter.
 37     from utils.hyperparameter_tuner import HyperparameterTuner
 38     hyperparameter_tuner = HyperparameterTuner(discrete_parameters, continuous_parameters, categorical_parameters,
 39                                                storage, study_name)
 40 
 41     # 3. Run main tuning function.
 42     hyperparameter_tuner.tune()
 43     # Watch out! This script neither deletes the study in DB nor deletes the database itself. If you are using
 44     # parallelized optimization, then you should care about deleting study in the database by yourself.
 45 
 46 
 47 if __name__ == "__main__":
 48     exit(main())